Punctuated evolution in the learned songs of African sunbirds.

Learned traits are thought to be subject to different evolutionary dynamics than other phenotypes, but their evolutionary tempo and mode has received little attention. Learned bird song has been thought to be subject to rapid and constant evolution. However, we know little about the evolutionary modes of learned song divergence over long timescales. Here, we provide evidence that aspects of the territorial songs of Eastern Afromontane sky island sunbirds Cinnyris evolve in a punctuated fashion, with periods of stasis of the order of hundreds of thousands of years or more, broken up by evolutionary pulses. Stasis in learned songs is inconsistent with learned traits being subject to constant or frequent change, as would be expected if selection does not constrain song phenotypes over evolutionary timescales. Learned song may instead follow a process resembling peak shifts on adaptive landscapes. While much research has focused on the potential for rapid evolution in bird song, our results suggest that selection can tightly constrain the evolution of learned songs over long timescales. More broadly, these results demonstrate that some aspects of highly variable, plastic traits can exhibit punctuated evolution, with stasis over long time periods.

Waist-to-Hip Ratio as Supernormal Stimuli: Effect of Contrapposto Pose and Viewing Angle.

In women, the waist-to-hip ratio (WHR) is an indicator of attractiveness, health, youthfulness, and reproductive potential. In the current study, we hypothesized that viewing angle and body postures influence the attractiveness of these forms based on the view dependency of WHR stimuli (vdWHR). Using eye tracking, we quantified the number of fixations and dwell time on 3D images of a female avatar in two different poses (standing and contrapposto) from eight viewing angles incrementing in 45 degrees of rotation. A total of 68 heterosexual individuals (25 men and 43 women) participated in the study. Results showed that the contrapposto pose was perceived as more attractive than the standing pose and that lower vdWHR sides of the stimuli attracted more first fixation, total fixations, and dwell time. Overall, the results supported that WHR is view-dependent and vdWHRs lower than optimal WHRs are supernormal stimuli that may generate peak shifts in responding. Results are discussed in terms of the attractiveness of women's movements (gaits and dance) and augmented artistic presentations.

Colorimetric captopril assay based on oxidative etching-directed morphology control of silver nanoprisms.

Oxidative etching is an effective approach to control the morphology of nanomaterials. Taking silver nanocrystals (AgNCs) as an example, oxidative etching-directed morphological transformation from a triangular prism shape to a disk shape is achieved and then applied to the determination of captopril. As a mediator, trace amount of halides play important roles in the shape-controlled evolution of AgNCs. Etching causes the color of the triangular silver nanoprims (AgNPRs) to change from blue to yellow on formation of round nanodisks. On addition of captopril, the oxidative etching of the AgNPRs is prevented owing to the protection by the drug via Ag-S bonding. In this case, the solution color does not change. This finding was used to design an assay of captopril that has a linear response in the 10-600 nM concentration range and a 2 nM limit of detection. This method also allows digital camera read-out. It was successfully applied to quality control of captopril in tablets. Graphical abstractOxidative etching-directed morphological transformation of silver nanocrystals is well manipulated and successfully applied in colorimetric determination of captopril in tablets.

Colorimetric determination of cytosine-rich ssDNA by silver(I)-modulated glucose oxidase-catalyzed growth of gold nanoparticles.

A colorimetric assay is described for determination of cytosine-rich ssDNA at physiological pH values. The working principle is based on (a) Ag(I) ion-induced formation of an i-motif structure, and (b) glucose oxidase-controlled growth of gold nanoparticles (AuNPs). The combination between Ag+ and cytosine-rich DNA can modulate the generation of H2O2 resulting from enzyme catalyzed glucose oxidation. Depending on the amount of H2O2 formed, the solution containing the AuNPs will turn red in the presence of cytosine-rich ssDNA but blue in the absence of such DNA if Ag+ is added before the formation of the red AuNPs. Upon addition of C-DNA at different concentrations, the peak shift (Δλ) of the AuNP solution relative to the SPR peak position (560 nm) in the absence of C-DNA is taken as the signal readout. The method shows a good linear response toward C-DNA over the range 10-200 nM with a detection limit of 2.7 nM. It may also be performed visually. The photometric assay is highly sensitive, specific, and rapid. The method is particularly attractive in terms of applications such as in human serum analysis, a colorimetric logic gate, and the calculation of binding constants for the interaction between Ag+ and glucose oxidase (GOx), and between Ag+ and cytosine-rich ssDNAs. Graphical abstract Schematic presentation of colorimetric detection of cytosine (C)-rich ssDNA (C-DNA) based on the modulation of the glucose oxidase (GOx)-catalyzed growth of gold nanoparticles (AuNPs) with Ag+ as the enzyme inhibitor.

Biased generalization of salient traits drives the evolution of warning signals.

The importance of receiver biases in shaping the evolution of many signalling systems is widely acknowledged. Here, we show that receiver bias can explain which traits evolve to become warning signals. For warning coloration, a generalization bias for a signalling trait can result from predators learning to discriminate unprofitable from profitable prey. However, because the colour patterns of prey are complex traits with multiple components, it is crucial to understand which of the many aspects of prey appearance evolve into signals. We provide experimental evidence that the more salient differences in prey traits give rise to greater generalization bias, corresponding to stronger selection towards trait exaggeration. Our results are based on experiments with domestic chickens as predators in a Skinner-box-like setting, and imply that the difference in appearance between profitable and unprofitable prey that is most rapidly learnt produces the greatest generalization bias. As a consequence, certain salient traits of unprofitable prey are selected towards exaggeration to even higher salience, driving the evolution of warning coloration. This general idea may also help to explain the evolution of many other striking signalling traits found in nature.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing.

BACKGROUND: Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift. RESULTS: Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes. CONCLUSIONS: We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

The Ecological Dynamics of Natural Selection: Traits and the Coevolution of Community Structure.

Natural selection has both genetic and ecological dynamics. The fitnesses of individuals change with their ecological context, and so the form and strength of selective agents change with abiotic factors and the phenotypes and abundances of interacting species. I use standard models of consumer-resource interactions to explore the ecological dynamics of natural selection and how various trait types influence these dynamics and the resulting structure of a community of coevolving species. Evolutionary optima favored by natural selection depend critically on the abundances of interacting species, and the traits of species can undergo dynamic cycling in limited areas of parameter space. The ecological dynamics of natural selection can also drive shifts from one adaptive peak to another, and these ecologically driven adaptive peak shifts are fundamental to the dynamics of niche differentiation. Moreover, this ecological differentiation is fostered in more productive and more benign environments where species interactions are stronger and where the selection gradients generated by species interactions are stronger. Finally, community structure resulting from coevolution depends fundamentally on the types of traits that underlie species interactions. The ecological dynamics of the process cannot be simplified, neglected, or ignored if we are to build a predictive theory of natural selection.

Fitness-valley crossing with generalized parent-offspring transmission.

Simple and ubiquitous gene interactions create rugged fitness landscapes composed of coadapted gene complexes separated by "valleys" of low fitness. Crossing such fitness valleys allows a population to escape suboptimal local fitness peaks to become better adapted. This is the premise of Sewall Wright's shifting balance process. Here we generalize the theory of fitness-valley crossing in the two-locus, bi-allelic case by allowing bias in parent-offspring transmission. This generalization extends the existing mathematical framework to genetic systems with segregation distortion and uniparental inheritance. Our results are also flexible enough to provide insight into shifts between alternate stable states in cultural systems with "transmission valleys". Using a semi-deterministic analysis and a stochastic diffusion approximation, we focus on the limiting step in valley crossing: the first appearance of the genotype on the new fitness peak whose lineage will eventually fix. We then apply our results to specific cases of segregation distortion, uniparental inheritance, and cultural transmission. Segregation distortion favouring mutant alleles facilitates crossing most when recombination and mutation are rare, i.e., scenarios where crossing is otherwise unlikely. Interactions with more mutable genes (e.g., uniparental inherited cytoplasmic elements) substantially reduce crossing times. Despite component traits being passed on poorly in the previous cultural background, small advantages in the transmission of a new combination of cultural traits can greatly facilitate a cultural transition. While peak shifts are unlikely under many of the common assumptions of population genetic theory, relaxing some of these assumptions can promote fitness-valley crossing.

A unique wheel-shaped exposed core LSPR-PCF sensor for dual-peak sensing: Applications in the optical communication bands, M-IR region and biosensing.

Photonic Crystal Fibers (PCF) effectiveness in practice decreases if the fabrication of the sensor becomes too complex. Keeping this in mind, we propose a one-of-a-kind wheel shaped PCF sensor with an exposed core containing only three air holes with exceptional sensing features. The sensor is equipped with dual plasmonic layers, Indium Tin Oxide (ITO, 10 % wt) and silver (Ag) with a coating of TiO2 to enhance the sensing capabilities and provide protection against oxidation. The sensor's distinctive configuration enables it to exhibit two distinct peaks within a range of refractive index from 1.32 to 1.38 for y-polarization and from 1.35 to 1.38 for x-polarization. The sensor specifications have been optimized to achieve the maximum levels of wavelength sensitivity (WS) and double peak shift sensitivity (DPSS). The sensor portrays a WS of 50,652 nm/RIU and the highest DPSS ever recorded, measuring 50,000 nm/RIU. Additionally, the sensor exhibits a significantly high scale of amplitude sensitivity (AS) of 1668.34 RIU-1 which is a very remarkable value considering silver as plasmonic material along with an outstanding figure of merit (FOM) of 1017.11 RIU-1. In addition, our sensor is able to manifest resolutions in the order of 10-6, demonstrating a resolution of 5.94 × 10-6 RIU with the deployment of amplitude interrogation method and 1.97 × 10-6 RIU with the wavelength interrogation method. The design spans an extensive spectrum, covering ultraviolet to mid-infrared wavelengths, enabling detection of biomolecules and biochemicals, along with operation in the optical communication band.

Judgement bias may be explained by shifts in stimulus response curves.

Judgement bias, or 'optimism' and 'pessimism', has been demonstrated across many taxa, yet the cognitive mechanisms underlying this behaviour remain unclear. In an optimism paradigm, animals are trained to an association, and, if given a positive experience, behave more favourably towards 'ambiguous' stimuli. We tested whether this effect could be explained by changes to stimulus response gradients by giving bees a task where their response was tested across a wider gradient of stimuli than typically tested. In line with previous work, we found that bees given a positive experience demonstrated judgement bias, being more likely to visit ambiguous stimuli. However, bees were also less likely to visit a stimulus on the other side of the rewarded stimulus (S+), and as such had a shifted stimulus response curve, showing a diminished peak shift response. In two follow-up experiments we tested the hypothesis that our manipulation altered bees' stimulus response curves via changes to the peak shift response by reducing peak shift in controls. We found that, in support of our hypothesis, elimination of peak shift also eliminated differences between treatments. Our results point towards a cognitive explanation of 'optimistic' behaviour in non-human animals and offer a new paradigm for considering emotion-like states.

Enhanced Degradability of the Apatite-Based Calcium Phosphate Cement Incorporated with Amorphous MgZnCa Alloy.

Degradability is vital for bone filling and plays an important role in bone regeneration. Evidence indicates that apatite-based calcium phosphate cement (ACPC) is a prospective biomaterial for bone repair with enhanced osteogenesis. However, poor degradability restricts their clinical application. In this study, MgZnCa-doped ACPC (MgZnCa/ACPC) composites were fabricated by adding 3 (wt) % amorphous MgZnCa powder in the solid phase of ACPC to enhance the biodegradation and bioactivity of the apatite ACPC. The chemical and the physical properties of the MgZnCa/ACPC composite were investigated and compared with the ACPC composite. The results showed that the incorporation of MgZnCa improved both the degradability and the compressive strength of the ACPC composite. X-ray diffraction and Fourier transform infrared spectrometry analysis suggested significant changes in the microstructures of the composites due to the incorporation and the anodic dissolution of MgZnCa alloy. These findings indicate that the MgZnCa/ACPC composite is capable of facilitating bone repair and regeneration by endowing favorable degradation property.

Design and analysis of a highly sensitive SPR based PCF biosensor with double step dual peak shift sensitivity.

This paper introduces a comprehensive study of a quad-cluster multi-functional Photonic Crystal Fiber (PCF) sensor where gold and Aluminum doped with zinc oxide (AZO) were used as plasmonic materials. A maximum Amplitude Sensitivity (AS) of 5336 RIU-1 and Wavelength Sensitivity (WS) of 40,500 nm/RIU in y pol was obtained incorporating Gold as plasmonic material. When AZO was included as the plasmonic material, AS of 3763 RIU-1 & WS of 9100 nm/RIU for y polarization were determined. The RI detecting range was increased from 1.32 to 1.43 to 1.19-1.42 after using AZO instead of Au that opens up a new horizon for detection. A novel detection technique, 'Double Step Dual Peak Shift Sensitivity (DS-DPSS)' was proposed in sensing temperature where highest sensitivity of 1.05 nm/°C having resolution of 0.095 °C for x pol. was achieved. Due to its diverse functionality, the suggested sensor represents a significant advancement in the detection of numerous analytes in biochemical applications.

Modelling generalisation gradients as augmented Gaussian functions.

Studying generalisation of associative learning requires analysis of response gradients measured over a continuous stimulus dimension. In human studies, there is often a high degree of individual variation in the gradients, making it difficult to draw conclusions about group-level trends with traditional statistical methods. Here, we demonstrate a novel method of analysing generalisation gradients based on hierarchical Bayesian curve-fitting. This method involves fitting an augmented (asymmetrical) Gaussian function to individual gradients and estimating its parameters in a hierarchical Bayesian framework. We show how the posteriors can be used to characterise group differences in generalisation and how classic generalisation phenomena such as peak shift and area shift can be measured and inferred. Estimation of descriptive parameters can provide a detailed and informative way of analysing human generalisation gradients.

Characterising interactions between co-infecting parasites using age-intensity profiles.

Interactions between co-infecting parasite species can impact transmission. Whether co-infection is beneficial or detrimental to a target parasite, and whether the mechanism involves changes in host susceptibility or parasite clearance, can be difficult to assess. We demonstrate the potential for host age-parasite intensity curves to allow assessment of these factors. A model is developed to generate predictions and test these predictions using helminth parasites of white-tailed deer (Odocoileus virginianus). We identify three beneficial interactions involving five helminth species, including susceptibility and clearance-based mechanisms. Our results suggest that analysis of age-intensity data represents a new tool for assessing the nature and strength of co-infecting parasite interactions.

Odds ratios and hurdle models: a long-term analysis of parasite infection patterns in endangered young-of-the-year suckers from Upper Klamath Lake, Oregon, USA.

We used odds ratios and a hurdle model to analyze parasite co-infections over 25 years on >20,000 young-of-the year of endangered Shortnose and Lost River Suckers. Host ecologies differed as did parasite infections. Shortnose Suckers were more likely to be caught inshore and 3-5 times more likely to have Bolbophorus spp. and Contracaecum sp. infections, and Lost River Suckers were more likely to be caught offshore and approximately three times more likely to have Lernaea cyprinacea infections. An observed peak shift seems likely to be due to a lower host size limit for Bolbophorus spp. (13.6 mm) compared with L. cyprinacea (23.4 mm). The large data set allowed us to generate strong hypotheses: (i) that a major marsh restoration project had unintended consequences that resulted in an increase in infections; (ii) that co-infection with Bolbophorus spp. increased the odds of infection by L. cyprinacea and Contracaecum sp.; (iii) that significant declines in the odds of infection over approximately 25 days were due to parasite-induced host mortality; (iv) that the fish's small size relative to L. cyprinacea and Contracaecum sp. might be directly lethal; (v) that the absence of L. cyprinacea infections in the early 1990s was associated with good year-class production of the suckers; and (vi) that parasites might increase the odds of vagrancy from the nursery ground.

Rule-based processes in generalisation and peak shift in human fear conditioning.

Two experiments explored the role of verbalisable rules in generalisation of human differential fear conditioning with electric shock as the aversive stimulus. Two circles of different sizes served as conditioned stimuli (CS+ and CS-), before testing with a range of circle sizes. In Experiment 1, shock expectancy ratings followed a peak-shifted unimodal gradient, with maximum ratings at a test value further along the dimension from CS+ in the opposite direction to CS-. However, differentiable gradients were observed when participants were divided on the basis of the rules they reported using during the task (linear and similarity). Experiment 2 was designed to counter the contradictory feedback arising from extinction testing by removing the shock electrodes during the test phase. A more linear overall gradient was observed, and sub-groups defined by self-reported rules showed distinct gradients that were congruent with their rules. These results indicate that rule-based processes are influential in generalisation of conditioned fear along simple stimulus dimensions, and may help explain generalisation phenomena that have traditionally been attributed to automatic, similarity-based processes.

Revisiting peak shift on an artificial dimension: Effects of stimulus variability on generalisation.

One of Mackintosh's many contributions to the comparative psychology of associative learning was in developing the distinction between the mental processes responsible for learning about features and learning about relations. His research on discrimination learning and generalisation served to highlight differences and commonalities in learning mechanisms across species and paradigms. In one such example, Wills and Mackintosh trained both pigeons and humans to discriminate between two categories of complex patterns comprising overlapping sets of abstract visual features. They demonstrated that pigeons and humans produced similar "peak-shifted" generalisation gradients when the proportion of shared features was systemically varied across a set of transfer stimuli, providing support for an elemental feature-based analysis of discrimination and generalisation. Here, we report a series of experiments inspired by this work, investigating the processes involved in post-discrimination generalisation in human category learning. We investigate how post-discrimination generalisation is affected by variability in the spatial arrangement and probability of occurrence of the visual features and develop an associative learning model that builds on Mackintosh's theoretical approach to elemental associative learning.

Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data.

INTRODUCTION: To aid the development of better algorithms for [Formula: see text]H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications. OBJECTIVE: We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites. METHODS: A pool of urine from healthy subjects was titrated in the range pH 2-12, standard [Formula: see text]H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule. RESULTS: The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range. CONCLUSIONS: Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in [Formula: see text]H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms.

Detection of Homologue and Isomer Vapors through Dynamic Reflection Spectra of Hollow Mesoporous Silica Sphere Photonic Crystals.

Vapors of homologues and isomers with very similar refractive indices can be easily distinguished by using dynamic reflection spectra (DRS) of hollow mesoporous silica sphere (HMSS) photonic crystals (PCs). Different diffusion behaviors render the HMSS PCs with a distinct response to different chemicals vapors, leading to diversity in the evolution of the reflection spectra and their DRS patterns. Therefore, by studying the geometric characteristics and color changes of color-filled contour maps, as well as the reflection peak shift speeds at different stages, even isomers such as n-butanol and iso-butanol, with a refractive index difference of only 0.001, can be recognized. The reflection peak shift speed depends on both the refractive index and the diffusion speed of chemicals. The proposed strategy provides a convenient, accurate, and low-cost method to detect vapors of homologues and isomers.

A Simple Computational Model of the Bee Mushroom Body Can Explain Seemingly Complex Forms of Olfactory Learning and Memory.

Honeybees are models for studying how animals with relatively small brains accomplish complex cognition, displaying seemingly advanced (or "non-elemental") learning phenomena involving multiple conditioned stimuli. These include "peak shift" [1-4]-where animals not only respond to entrained stimuli, but respond even more strongly to similar ones that are farther away from non-rewarding stimuli. Bees also display negative and positive patterning discrimination [5], responding in opposite ways to mixtures of two odors than to individual odors. Since Pavlov, it has often been assumed that such phenomena are more complex than simple associate learning. We present a model of connections between olfactory sensory input and bees' mushroom bodies [6], incorporating empirically determined properties of mushroom body circuitry (random connectivity [7], sparse coding [8], and synaptic plasticity [9, 10]). We chose not to optimize the model's parameters to replicate specific behavioral phenomena, because we were interested in the emergent cognitive capacities that would pop out of a network constructed solely based on empirical neuroscientific information and plausible assumptions for unknown parameters. We demonstrate that the circuitry mediating "simple" associative learning can also replicate the various non-elemental forms of learning mentioned above and can effectively multi-task by replicating a range of different learning feats. We found that PN-KC synaptic plasticity is crucial in controlling the generalization-discrimination trade-off-it facilitates peak shift and hinders patterning discrimination-and that PN-to-KC connection number can affect this trade-off. These findings question the notion that forms of learning that have been regarded as "higher order" are computationally more complex than "simple" associative learning.